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6下载:
用于故障诊断的PCA方法例程5个(含KPCA),利用PCA(主元分析)方法或者KPCA方法,进行工业系统的故障诊断程序,有详细的注释说明-PCA method for fault diagnosis routine five (including KPCA), using PCA (principal component analysis) method or KPCA method, industrial process fault diagnosis, a detailed explanat
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主分量分析,用于高维数据降维或提取目标特征。程序精简,效率高.
-Principal Component Analysis is used to make data dimensionality reduction or extract target characteristics。
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基于主成份分析(PCA)的人脸识别算法MATLAB程序的实现。机器视觉的作业,内附人脸识别的matlab程序,和人脸库,还有作业的详细要求,以及格式示例和部分参考文献。-Based on principal component analysis (PCA) of the face recognition algorithm MATLAB program implementation. Machine vision operations, included face recognition mat
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主成分分析,可以用来做人脸识别的程序,方便,快捷-Principal component analysis, face recognition can be used to do the procedure, convenient and fast
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主成分分析ppt。对做图像分析以及融合很有帮助。从别处转来的。希望有用。-Principal component analysis ppt. Right to do image analysis and fusion helpful. Have been transferred there. Want to be useful.
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PCA主成分分析的标准MATLAB程序,可通用,非常有效-PCA principal component analysis of the standard MATLAB procedures, can be universal, very effective
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主元分析 (Principal Component Analysis, PCA) 又叫:Karhunen-Loeve变换
(KLT)、Hotelling变换。
假设已经从图象已经缩放为N*M大小。
m幅N*M大小的图象Xi作为n*1列向量看待-PCA (Principal Component Analysis, PCA) also known as: Karhunen-Loeve Transform (KLT), Hotelling transform.
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主分量分析(PCA ) 是统计学中分析数据的一种有效的方法, 可以将数据从高维数据空间变换到低维特征空间, 因而
可以用于数据的特征提取及压缩等方面。在该文的形状识别系统中, 用PCA 法提取图像的形状特征, 能够较好地满足识别
层的输入要求。在识别层研究了3 种识别方法: 最近邻法则、BP 网络及协同神经网络方法, 均取得了满意的实验效果。-Principal component analysis (PCA) is a statistical analysis of data in a
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关于主成份分析的vc++源码,注释详细,以KL变换为基础,使特征将维。-Principal component analysis on the vc++ Source, comments, detail, based on KL transform, so that will be characterized by Victoria.
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matlab PCA,主成分分析经典算法-matlab PCA, principal component analysis of the classic algorithm
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PCA主成份分析算法的MATLAB程序源码.测试是以费雷数据库的灰度等级部分.-PCA principal component analysis algorithm MATLAB program source code. Test is gray ferret grade part of the database.
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主成分分析程序,可用于数据降维及特征提取。-Principal component analysis procedures, can be used for data dimensionality reduction and feature extraction.
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该程序包实现了模式识别中的两个特征提取算法,主成分分析PCA和线性判别分析LDA。采用C++语言编写,开发环境VS。 程序包还提供了两个测试样本文件。-The package to achieve the recognition of the two feature extraction algorithm, principal component analysis PCA and linear discriminant analysis LDA. Using C++ language, dev
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主分量分析方法的应用越来越多,它是怎样实现的呢?本代码就是其实现过程。-Principal component analysis method, more and more how it is achieved it? This code is the implementation process.
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PCA主成分分析用于人脸识别,提取特征值特征向量。有ORL人脸库。-PCA principal component analysis for face recognition, extraction Eigenvalue eigenvector. Have ORL face database.
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主成分分析程序包,包括主成分分析和独立主成分分析两个程序源代码。-Principal component analysis package, including principal component analysis principal component analysis and independent source code for both procedures.
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这是一个MATLAB工具箱包括32个降维程序,主要包括 pca,lda,MDS等十几个程序包,对于图像处理非常具有参考价值- ,This Matlab toolbox implements 32 techniques for dimensionality reduction. These techniques are all available through the COMPUTE_MAPPING function or trhough the GUI. The following techn
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用主成分分析(PCA)作融合(FUSION)的文献,英文的,从IEEE下载得到-Using principal component analysis (PCA) for fusion (FUSION) literature, English, and download from the IEEE be
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PCA主元分析后用神经网络预测,A/S含量,PCA算法实现,与神经网络-PCA principal component analysis using neural network prediction, A/S content, PCA algorithm, and neural network
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主成分分析法能较好地实现图像融合,特别是空间特征保持非常好,但光谱特征的保持性要差一些。-Principal Component Analysis to achieve a better image fusion, in particular, to maintain a very good spatial characteristics, but to maintain the spectral characteristics of some worse.
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